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Related Experiment Video

Updated: Dec 6, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.8K

A Novel Active Contour Model for Noisy Image Segmentation based on Adaptive Fractional Order Differentiation.

Meng-Meng Li, Bing-Zhao Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 13, 2020
    PubMed
    Summary

    This study introduces a new active contour model to improve image segmentation accuracy by effectively handling various noise types. The model enhances segmentation results in noisy images, offering a more robust solution for image processing tasks.

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    Area of Science:

    • Image Processing
    • Computer Vision
    • Computational Mathematics

    Background:

    • Image noise, including Gaussian, speckled, and salt-and-pepper types, significantly degrades image quality.
    • Noise in images presents a major challenge for accurate image segmentation, often leading to imprecise results.
    • Existing segmentation methods struggle to effectively mitigate the impact of noise on segmented outputs.

    Purpose of the Study:

    • To propose a novel active contour model designed to overcome the limitations of noise in image segmentation.
    • To develop a robust model capable of accurately segmenting images corrupted by various noise patterns.
    • To enhance the precision and reliability of image segmentation in the presence of noise.

    Main Methods:

    • A new active contour model incorporating fitting, regularization, and penalty terms.

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.8K
  • The fitting term utilizes a Gaussian kernel function and adaptive fractional order differentiation for pixel-specific processing.
  • Regularization and penalty terms are included to ensure curve smoothness and stable evolution during segmentation.

    • Main Results:

    • The proposed active contour model demonstrates effectiveness in segmenting noisy images.
    • Experimental comparisons validate the model's efficiency and superior performance over existing methods.
    • The adaptive fractional order differentiation successfully addresses varying noise characteristics.

    Conclusions:

    • The novel active contour model provides a significant advancement in noise-robust image segmentation.
    • The adaptive approach in the fitting term is key to handling diverse noise types effectively.
    • The model offers a reliable solution for accurate image segmentation in practical applications.